finalize 0.7.8

chainladder/core/common.py

@@ -33,6 +33,13 @@ def _get_full_triangle(full_expectation_, triangle_):
 
 class Common:
     """ Class that contains common properties of a "fitted" Triangle. """
+    @property
+    def has_ldf(self):
+        if hasattr(self, "ldf_"):
+            return True
+        else:
+            return False
+
 
     @property
     def cdf_(self):

chainladder/core/triangle.py

@@ -217,24 +217,28 @@ def incr_to_cum(self, inplace=False):
             if not self.is_cumulative:
                 if self.is_pattern:
                     values = xp.nan_to_num(self.values[..., ::-1])
+                    if self.array_backend == "sparse":
+                        xp = np
+                        values = self.set_backend('numpy').values
                     values[values == 0] = 1.0
                     values = xp.cumprod(values, -1)[..., ::-1]
                     self.values = values = values * self.nan_triangle
+                    if self.array_backend == "sparse":
+                        self.values = self.get_array_module()(self.values)
                 else:
-                    ddims = self.ddims
                     if self.array_backend != "sparse":
                         self.values = (
                             num_to_nan(xp.cumsum(xp.nan_to_num(self.values), 3))
                             * self.nan_triangle[None, None, ...]
                         )
                     else:
-                        l1 = lambda i: self.iloc[..., 0 : (i + 1)]
-                        l2 = lambda i: l1(i) * self.nan_triangle[..., i : i + 1]
-                        l3 = lambda i: l2(i).sum(3, auto_sparse=False, keepdims=True)
-                        self = concat(
-                            [l3(i).rename(3, [i]) for i in range(self.shape[-1])], 3,
-                        )
-                    self.ddims = ddims
+                        values = xp.nan_to_num(self.values)
+                        nan_triangle = xp.nan_to_num(self.nan_triangle)
+                        l1 = lambda i: values[..., 0 : (i + 1)]
+                        l2 = lambda i: l1(i) * nan_triangle[..., i : i + 1]
+                        l3 = lambda i: l2(i).sum(3, keepdims=True)
+                        out = [l3(i) for i in range(self.shape[-1])]
+                        self.values = num_to_nan(xp.concatenate(out, axis=3))
                 self.is_cumulative = True
             return self
         else:

chainladder/development/clark.py

@@ -90,7 +90,7 @@ def G_(self, age):
         if type(age) == list:
             age = xp.array([age]).astype("float64")
         obj = self.incremental_act_.copy()
-        obj.odims = xp.array(["(All)"])
+        obj.odims = obj.odims[0:1]
         obj.values = 1 / self._G(age)
         obj.ddims = age
         return obj
@@ -185,8 +185,11 @@ def solver(x):
         cdf = self._G(
             latest_age - age_offset, theta=params[..., 1:2], omega=params[..., 0:1]
         )
-        obj.values = xp.repeat(cdf[..., :-1] / cdf[..., 1:], len(obj.odims), 2)
+        obj.values = cdf[..., :-1] / cdf[..., 1:]
         obj.ddims = X.link_ratio.ddims
+        obj.odims = obj.odims[0:1]
+        obj.is_pattern = True
+        obj.is_cumulative = False
         obj._set_slicers()
         self.ldf_ = obj
         self.ldf_.valuation_date = pd.to_datetime(ULT_VAL)

chainladder/development/munich.py

@@ -111,6 +111,7 @@ def fit(self, X, y=None, sample_weight=None):
         self.ldf_ = self._set_ldf(
             obj, self._get_mcl_cdf(obj, self.munich_full_triangle_)
         )
+        self.ldf_.is_cumulative = False
         self.ldf_.valuation_date = pd.to_datetime(ULT_VAL)
         self._map = {
             (list(X.columns).index(x)): (num % 2, num // 2)

chainladder/methods/base.py

@@ -140,7 +140,7 @@ def _include_process_variance(self):
         return process_var
 
     def validate_weight(self, X, sample_weight):
-        if sample_weight and X.shape[:-1] != sample_weight.shape[:-1]:
+        if sample_weight and X.shape[:-1] != sample_weight.shape[:-1] and sample_weight.shape[2] != 1 and sample_weight.shape[0] > 1:
             warnings.warn(
                 "X and sample_weight are not aligned.  Broadcasting may occur.\n"
             )

chainladder/utils/templates/triangle.yaml

@@ -92,7 +92,7 @@ Sheet:
       {% endfor %}
       {% endif %}
 
-      {% if triangle.ldf_ %}
+      {% if triangle.has_ldf %}
       - CSpacer:
           height: 1
       - Series:

docs/auto_examples/index.rst

@@ -370,14 +370,14 @@ Examples
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
-     :alt: Stochastic Bornhuetter Ferguson
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
 
-     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
 
 .. raw:: html
 
@@ -387,18 +387,18 @@ Examples
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_stochastic_bornferg
+   /auto_examples/plot_bootstrap
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the normalized residual plots in Clarks LDF Curve-Fit...">
+    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_resid_thumb.png
-     :alt: Clark Residual Plots
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
+     :alt: Stochastic Bornhuetter Ferguson
 
-     :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
+     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
 
 .. raw:: html
 
@@ -408,18 +408,18 @@ Examples
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_clarkldf_resid
+   /auto_examples/plot_stochastic_bornferg
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to recreate the normalized residual plots in Clarks LDF Curve-Fit...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
-     :alt: ODP Bootstrap Example
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_clarkldf_resid_thumb.png
+     :alt: Clark Residual Plots
 
-     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
+     :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py`
 
 .. raw:: html
 
@@ -429,7 +429,7 @@ Examples
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_bootstrap
+   /auto_examples/plot_clarkldf_resid
 
 .. raw:: html
 

docs/auto_examples/plot_bootstrap.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import chainladder as cl\nimport matplotlib.pyplot as plt\n\n#  Grab a Triangle\ntri = cl.load_sample('genins')\n# Generate bootstrap samples\nsims = cl.BootstrapODPSample().fit_transform(tri)\n# Calculate LDF for each simulation\nsim_ldf = cl.Development().fit(sims).ldf_\nsim_ldf = sim_ldf[sim_ldf.origin==sim_ldf.origin.max()]\n\n# Plot the Data\nfig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))\n# Plot 1\ntri.T.plot(ax=ax00, grid=True).set(title='Raw Data', xlabel='Development', ylabel='Incurred')\n# Plot 2\nsims.mean().T.plot(ax=ax01, grid=True).set(title='Mean Simulation', xlabel='Development')\n# Plot 3\nsim_ldf.T.plot(legend=False, color='lightgray', ax=ax10, grid=True).set(\n    title='Simulated LDF', xlabel='Development', ylabel='LDF')\ncl.Development().fit(tri).ldf_.drop_duplicates().T.plot(\n    legend=False, color='red', ax=ax10, grid=True)\n# Plot 4\nsim_ldf.T.loc['12-24'].plot(\n    kind='hist', bins=50, alpha=0.5, ax=ax11 , grid=True).set(\n    title='Age 12-24 LDF Distribution', xlabel='LDF');"
+        "import chainladder as cl\nimport matplotlib.pyplot as plt\n\n#  Grab a Triangle\ntri = cl.load_sample('genins')\n# Generate bootstrap samples\nsims = cl.BootstrapODPSample().fit_transform(tri)\n# Calculate LDF for each simulation\nsim_ldf = cl.Development().fit(sims).ldf_\n\n# Plot the Data\nfig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))\n# Plot 1\ntri.T.plot(ax=ax00, grid=True).set(title='Raw Data', xlabel='Development', ylabel='Incurred')\n# Plot 2\nsims.mean().T.plot(ax=ax01, grid=True).set(title='Mean Simulation', xlabel='Development')\n# Plot 3\nsim_ldf.T.plot(legend=False, color='lightgray', ax=ax10, grid=True).set(\n    title='Simulated LDF', xlabel='Development', ylabel='LDF')\ncl.Development().fit(tri).ldf_.drop_duplicates().T.plot(\n    legend=False, color='red', ax=ax10, grid=True)\n# Plot 4\nsim_ldf.T.loc['12-24'].plot(\n    kind='hist', bins=50, alpha=0.5, ax=ax11 , grid=True).set(\n    title='Age 12-24 LDF Distribution', xlabel='LDF');"
       ]
     }
   ],

docs/auto_examples/plot_bootstrap.py

@@ -15,7 +15,6 @@
 sims = cl.BootstrapODPSample().fit_transform(tri)
 # Calculate LDF for each simulation
 sim_ldf = cl.Development().fit(sims).ldf_
-sim_ldf = sim_ldf[sim_ldf.origin==sim_ldf.origin.max()]
 
 # Plot the Data
 fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))

docs/auto_examples/plot_bootstrap.py.md5

@@ -1 +1 @@
-d69313408bdb72d6709775b307368f26
+a4b7163692b2b29b03d9eed31d4a4d32

docs/auto_examples/plot_bootstrap.rst

@@ -29,7 +29,7 @@ Bootstrap sampler and get various properties about parameter uncertainty.
 
  .. code-block:: none
 
-    c:\users\jboga\onedrive\documents\github\chainladder-python\chainladder\core\pandas.py:264: RuntimeWarning: Mean of empty slice
+    c:\users\jboga\onedrive\documents\github\chainladder-python\chainladder\core\pandas.py:280: RuntimeWarning: Mean of empty slice
       obj.values = func(obj.values, axis=axis, *args, **kwargs)
 
     [Text(0.5, 0, 'LDF'), Text(0.5, 1.0, 'Age 12-24 LDF Distribution')]
@@ -52,7 +52,6 @@ Bootstrap sampler and get various properties about parameter uncertainty.
     sims = cl.BootstrapODPSample().fit_transform(tri)
     # Calculate LDF for each simulation
     sim_ldf = cl.Development().fit(sims).ldf_
-    sim_ldf = sim_ldf[sim_ldf.origin==sim_ldf.origin.max()]
 
     # Plot the Data
     fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))
@@ -73,7 +72,7 @@ Bootstrap sampler and get various properties about parameter uncertainty.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  2.300 seconds)
+   **Total running time of the script:** ( 0 minutes  2.244 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_bootstrap.py:

docs/auto_examples/plot_clarkldf.rst

@@ -64,7 +64,7 @@ age.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  1.304 seconds)
+   **Total running time of the script:** ( 0 minutes  0.956 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_clarkldf.py:

docs/auto_examples/plot_clarkldf_resid.rst

@@ -74,7 +74,7 @@ Clarks LDF Curve-Fitting paper (2003).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.376 seconds)
+   **Total running time of the script:** ( 0 minutes  0.345 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_clarkldf_resid.py:

docs/auto_examples/sg_execution_times.rst

@@ -5,12 +5,14 @@
 
 Computation times
 =================
-**00:02.058** total execution time for **auto_examples** files:
+**00:03.546** total execution time for **auto_examples** files:
 
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py` (``plot_stochastic_bornferg.py``)   | 00:01.796 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_bootstrap.py` (``plot_bootstrap.py``)                       | 00:02.244 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py` (``plot_bf_apriori_from_cl.py``)     | 00:00.261 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_clarkldf.py` (``plot_clarkldf.py``)                         | 00:00.956 | 0.0 MB |
++-----------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py` (``plot_clarkldf_resid.py``)             | 00:00.345 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_ave_analysis.py` (``plot_ave_analysis.py``)                 | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
@@ -20,18 +22,14 @@ Computation times
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_berqsherm_closure.py` (``plot_berqsherm_closure.py``)       | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_bondy_sensitivity.py` (``plot_bondy_sensitivity.py``)       | 00:00.000 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_bf_apriori_from_cl.py` (``plot_bf_apriori_from_cl.py``)     | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_bootstrap.py` (``plot_bootstrap.py``)                       | 00:00.000 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_bondy_sensitivity.py` (``plot_bondy_sensitivity.py``)       | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_bootstrap_comparison.py` (``plot_bootstrap_comparison.py``) | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_capecod.py` (``plot_capecod.py``)                           | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_clarkldf.py` (``plot_clarkldf.py``)                         | 00:00.000 | 0.0 MB |
-+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_clarkldf_resid.py` (``plot_clarkldf_resid.py``)             | 00:00.000 | 0.0 MB |
-+-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_development_periods.py` (``plot_development_periods.py``)   | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_exhibits.py` (``plot_exhibits.py``)                         | 00:00.000 | 0.0 MB |
@@ -50,6 +48,8 @@ Computation times
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_munich_resid.py` (``plot_munich_resid.py``)                 | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py` (``plot_stochastic_bornferg.py``)   | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_tailcurve_compare.py` (``plot_tailcurve_compare.py``)       | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_triangle_from_pandas.py` (``plot_triangle_from_pandas.py``) | 00:00.000 | 0.0 MB |

docs/modules/generated/chainladder.BootstrapODPSample.examples

@@ -5,19 +5,19 @@ Examples using ``chainladder.BootstrapODPSample``
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="There are several aspects of the chainladder module that are demonstrated with this example.">
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">
 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_bornferg_thumb.png
-     :alt: Stochastic Bornhuetter Ferguson
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_bootstrap_thumb.png
+     :alt: ODP Bootstrap Example
 
-     :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+     :ref:`sphx_glr_auto_examples_plot_bootstrap.py`
 
 .. raw:: html
 
     </div>
 
 .. only:: not html
 
- * :ref:`sphx_glr_auto_examples_plot_stochastic_bornferg.py`
+ * :ref:`sphx_glr_auto_examples_plot_bootstrap.py`

docs/modules/generated/chainladder.Common.T.examples

@@ -3,25 +3,6 @@
 Examples using ``chainladder.Common.T``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="All IBNR models spin off several results triangles including inbr_, ultimate_, full_expectation...">
-
-.. only:: html
-
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_ibnr_runoff_thumb.png
-     :alt: IBNR Runoff
-
-     :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
-.. raw:: html
-
-    </div>
-
-.. only:: not html
-
- * :ref:`sphx_glr_auto_examples_plot_ibnr_runoff.py`
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and g...">